Systems and methods for determining a vehicle is at an elevated risk for an animal collision

ABSTRACT

Methods and systems for analyzing environment data to determine whether a vehicle operator is at an elevated risk for an animal collision are provided. According to certain aspects, an insurance provider may assess elevated risk according to various factors and, if it is determined that the vehicle operator is at an elevated risk for an animal collision, the insurance provider may generate a warning and wirelessly communicate the warning to the vehicle operator. The factors analyzed may include past accident, driver characteristic, weather, calendar, time of day, animal, seasonal, and/or other information. The vehicle operator may be notified of the risk and optionally presented with tips to mitigate the risk. The vehicle operator may be notified of the risk by a mobile device and/or the vehicle, such as from a vehicle communication and control system. Animal collision avoidance functionality may be used to adjust insurance premiums, rates, or rewards.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefit of the filing date of U.S. ProvisionalPatent Application 62/005,644, filed May 30, 2014. The contents of U.S.Provisional Patent Application 62/005,644 are expressly incorporatedherein by reference in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to improving vehicle safetyand, more particularly, to systems and methods for determining a vehicleis at an elevated risk for an animal collision based on variousenvironment data.

BACKGROUND

Vehicle or automobile insurance exists to provide financial protectionagainst physical damage and/or bodily injury resulting from trafficaccidents and against liability that could arise therefrom. One commontype of traffic accident occurs when a customer's vehicle collides withan animal, such as a deer crossing a road. Certain animal collisions mayresult in significant bodily injury or even death. Further, when acustomer's vehicle collides with an animal, insurance claims may begenerated to cover the expenses associated with vehicle repair and/orpersonal injury.

The present embodiments may, inter alia, alleviate the foregoing risks,such as the risk of bodily injury, vehicular damage, insurance claims,and/or other risks.

SUMMARY

A system and method may reduce vehicle collisions with animals bywarning a vehicle and/or occupants thereof of certain risks. A mobiledevice and/or vehicle control system may alert the vehicle and/orvehicle occupants that a moving vehicle is about to enter an areaassociated with a higher than average, or otherwise high likelihood, ofanimals and/or collisions therewith. As a result, the vehicle and/ordriver may utilize more caution, lower speed, and/or take otherappropriate actions. Once the vehicle passes through the area ofelevated risk of animal collision, the mobile device and/or vehiclecontrol system may inform the vehicle and/or vehicle occupants so thatnormal vehicle operation may resume. Additionally, customer input ordata may be utilized to improve the accuracy of the warning system.

In one aspect, a computer-implemented method of processing vehiclecollision risk information may be provided. The method may includereceiving, at a hardware server, vehicle data indicating at least alocation of a vehicle and accessing, by a processor, environment dataassociated with the location of the vehicle. The method may furtherinclude, based upon the environment data, determining, by the processor,that the vehicle is at an elevated risk for an animal collision.Additionally, the method may include generating, by the processor, anotification indicating the elevated risk and communicating, via acommunications network, the notification to the vehicle. The method mayinclude additional, fewer, or alternate actions, including thosediscussed elsewhere herein.

In another aspect, a system for processing vehicle collision riskinformation may be provided. The system may include a communicationmodule adapted to communicate data, a memory adapted to storenon-transitory computer executable instructions, a hardware server tostore environment data, and a processor adapted to interface with thecommunication module. The processor may be configured to execute thenon-transitory computer executable instructions to cause the processorto receive, via the communication module, vehicle data indicating atleast a location of a vehicle. The processor may be further configuredto access, via the processor, at least a portion of the environment dataassociated with the location of the vehicle and based upon theenvironment data, determine, via the processor, that the vehicle is atan elevated risk for an animal collision. Additionally, the processormay be configured to generate, via the processor, a notificationindicating the elevated risk, and communicate, via the communicationmodule, the notification to the vehicle. The system may includeadditional, fewer, or alternate components, including those discussedelsewhere herein.

In another aspect, a computer-implemented method of issuing an alertassociated with a likelihood of a vehicle-animal collision may beprovided. The method may include, via one or more processors, (i)predicting (1) a geographical scope, (2) a temporal scope, and/or (3) aseasonal scope of an area at high risk of being associated with avehicle-animal collision; (ii) monitoring or identifying a currentlocation of a mobile device; (iii) monitoring or identifying a currenttime of day; (iv) monitoring or identifying a current time of year;and/or (v) when (a) the current location of the mobile device matches orfalls within the geographical scope of the area at high risk, (b) thecurrent time matches, falls within, or coincides with the temporal scopeof the area at high risk, and/or (c) the current time of year matches,falls within, or coincides with the seasonal scope of the area at highrisk causing the mobile device to issue an alert to the user. The methodmay include additional, fewer, or alternate actions, including thosediscussed elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The figures described below depict various aspects of the system andmethods disclosed herein. It should be understood that each figuredepicts an embodiment of a particular aspect of the disclosed system andmethods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 depicts an example environment including components and entitiesassociated with determining that the vehicle is at an elevated risk foran animal collision in accordance with some embodiments.

FIG. 2 depicts an example diagram associated with determining andcommunicating that a vehicle is at an elevated risk for an animalcollision in accordance with some embodiments.

FIG. 3 depicts an exemplary alert displayable by a customer device inaccordance with some embodiments.

FIG. 4 depicts an exemplary animal collision report form to betransmitted if an animal collision occurs in accordance with someembodiments.

FIG. 5 depicts a flow diagram associated with an insurance providerdetermining and communicating that a vehicle is at an elevated risk foran animal collision in accordance with some embodiments.

FIG. 6 is a block diagram of a hardware server in accordance with someembodiments.

DETAILED DESCRIPTION

The present embodiments relate to, inter alia, creating a database ofanimal induced vehicle accidents, such as accidents involvingautomobiles striking deer or other animals. Each accident entry into thedatabase may include associated information, such as information relatedto: accident location (e.g., GPS (Global Positioning System), GNSS(Global Navigation Satellite System), latitude/longitude coordinate,road and mile marker, or other location information); time of accident;day of year of the accident; weather at the time of the accident; roador road type on which the accident occurred; driver information orcharacteristics of the driver involved in the accident; type of animalinvolved in the accident; type of vehicle involved in the accident;geography surrounding the area, or in the vicinity, of the accident(such as hills, flat, river or creek bed, river or creek crossing,heavily or lightly wooded, open land, etc.); nearby fields (such ascorn, wheat, or soybean fields, pasture or brush, etc.); real time orpredicted time of harvest (such as corn or soybeans being combined inthe fall); wetlands; animal preserves; animal tendencies orcharacteristics (such as animal mating season, migratory tendencies,animal movement and eating tendencies, hunting season(s)); events thatmay impact traffic; real time or predicted traffic conditions; real timeor anticipated road construction; and/or other information.

Once the database of vehicle-animal accidents is built, such as by usingthe past vehicle-animal accident data for several years, one or morealgorithms may analyze the accident data to identify and/or predictcertain areas and/or characteristics associated with a likelihood of ahigh risk of a vehicle-animal collision. In one embodiment, a processormay build one or more probabilistic or statistical models to helpidentify the likelihood of an animal collision event. The models may usethe types of information identified above (such as accident location,time, day of year, weather, geography, etc.) associated with eachvehicle-animal accident stored in the database or other memory unit.Linear regression or algorithms, or other modeling techniques, may beused (e.g., logistic regression, generalized linear models, neuralnetworks, Bayesian networks, Gaussian regression, ensemble methods,and/or others).

During use, an application on a mobile device, such as a smart phone,cell phone, tablet, phablet, laptop, notebook, PDA (personal digitalassistant), pager, smart watch, hand-held computing device, wearableelectronic device, computer, access point, node, relay, other devicecapable of wireless RF (radio frequency) communication, etc., or on avehicle system (such as a smart car or other vehicle-based computer orcontrol system), may monitor the position of the vehicle and/or themobile device (and thus the position of the vehicle in which the mobiledevice is traveling). The application may remotely or locally access themodels and/or database.

If the current conditions associated with the mobile device, such asmobile device or vehicle location, time of day, day of year, weather,visibility, and other conditions match those associated with a high riskevent identified in the model and/or database, an audible, vibrating,visual, or other type of alert or warning may be issued to warn thedriver or other user of the mobile device that the vehicle is presentlyapproaching, or is currently within, a high risk area having arelatively high likelihood of vehicle-animal collisions.

As an example, during deer mating season in Wisconsin, typically inNovember, deer may be especially active. In areas of high deer travel,such as along roads that pass though wooded areas, or on roads that passover creeks, streams, or rivers, etc., the risk of a deer strike eventmay be especially high. The database may also identify that accidentsare also more likely to occur during early evening hours.

Thus, by matching the vehicle's current location; the time of day; theday of year; and/or geography information with pre-identified areas ofhigh risk that are stored in a memory, a mobile device may issue anaudible alert, such as “High risk of animal collision for the next twomiles.” Preferably, the alert is non-distracting, such as an audiblealert or vibration.

Although above mentioned an automobile-deer collision, the presentembodiments may include automobile collisions with other types ofanimals, such as wild pigs, elk, bores, moose, birds, pheasants, ducks,geese, turkeys, cows, horses, raccoons, dogs, cats, pigs, bears,chickens, foxes, armadillos, alligators, lions, tigers, etc. The presentembodiments may also include other types of vehicles, such as airplanesor boats. For instance, airplanes may collide with various types ofbirds, and boats may collide with various types of fish, such as whales.

I. Exemplary Risk Alleviation

The novel systems and methods disclosed herein relate generally todetermining that a vehicle is at risk of experiencing an animalcollision. In particular, the systems and methods analyze vehicle andenvironment data associated with a vehicle to determine that the vehicleis at an elevated risk of an animal collision, and notify a customer orvehicle operator of the elevated risk. According to certain aspects, ifthe customer experiences an animal collision, the customer may fill outand send an incident report to an insurance provider, which may use thedata to improve its models for identifying animal collision risks.

The systems and methods therefore offer a benefit to customers byenabling drivers to receive sufficient warning that an animal collisionmay occur. By alerting drivers about the risk of an animal collision,drivers are more informed about the risks in operating the vehicle andmay be more likely to modify driving behavior to avoid a potentialanimal collision. Further, insurance providers may experience a reducednumber of claims as a result of fewer animal collisions, thus reducingtheir overall liabilities. Additionally, the systems and methodsdescribed herein may further the environmentalist goal of reducingunnatural animal deaths.

As used herein, it should be appreciated that the term “animal” mayrefer to any type of animal that may interfere with the path of avehicle. Some exemplary animals include deer, moose, elk, armadillo,boar, coyote, and bear. The animal may also be aquatic (e.g., manatee orcrocodile) or avian (e.g., goose or pigeon). The systems and methodsdiscussed herein envision assessing risks for a plurality of vehicletypes to collide with a plurality of types of animals. Any specific riskassessment for a vehicle-animal collision described herein is meant tobe exemplary and not limiting.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment, asdescribing every possible embodiment would be impractical, if notimpossible. One could implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

II. Exemplary Environment for Determining Elevated Risk

FIG. 1 depicts an example environment 100 associated with determiningthat the vehicle is at an elevated risk for an animal collision.Although FIG. 1 depicts certain entities, components, and devices, itshould be appreciated that additional or alternate entities andcomponents are envisioned.

As illustrated in FIG. 1, the environment 100 includes a vehicle 105that may be any type of car, automobile, truck, motorcycle, fleet ofvehicles, marine vessel, aeronautical vessel, or other vehicle capableof being driven or operated by a driver or operator. The vehicle 105 mayhave an electronic device 106 associated therewith. In some cases, theelectronic device 106 may be an on-board infotainment console inside thevehicle 105, such as part of an original equipment manufacturer (OEM)installation on the vehicle 105. In other cases, the electronic device106 may belong to a driver or operator of the vehicle 105 (generally, a“vehicle operator”). For example, the electronic device 106 may be asmartphone of the vehicle operator. It should be appreciated that othertypes of electronic devices are envisioned, such as notebook computers,tablets, GPS devices, smart watches, and/or the like.

The electronic device 106 may be configured to communicate with aninsurance provider 110 via a network 120. The network 120 may facilitateany type of data communication via any standard or technology (e.g.,GSM, CDMA, TDMA, WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB, IEEE 802including Ethernet, WiMAX, and/or others). In general, an insuranceprovider may be an entity capable of assessing risks of variousliability-generating incidents occurring. Although FIG. 1 depicts theinsurance provider 110, it should be appreciated that other entitiesthat are capable of assessing risk are envisioned. For example, ageneral risk assessing entity may be any individual, group ofindividuals, company, corporation, or other type of entity that canassess animal collision risks based on certain information and provideinformation pertaining to any identified risks to the vehicle 105.According to embodiments, the insurance provider 110 may include one ormore hardware server(s) 125 configured to facilitate the functionalitiesas discussed herein. Although FIG. 1 depicts the hardware server 125 asa part of the insurance provider 110, it should be appreciated that thehardware server 125 may be separate from (and connected to or accessibleby) the insurance provider 110 or a general risk assessing entity.

According to the present embodiments, the insurance provider 110 maygenerate and communicate an alert or notification to the electronicdevice 106, where the alert or notification warns or notifies thevehicle operator that the vehicle 105 may be at an elevated risk for ananimal collision. In particular, the alert or notification may includeinformation identifying a specific type of animal, a reason for theelevated risk, and/or any other relevant information. The alert ornotification may additionally include information pertaining to thespecific factors used to determine that the vehicle 105 may be at anelevated risk for an animal collision. For example, the alert ornotification may state that a vehicle operator is at an elevated riskfor a collision with a coyote due to the presence of nearby water.

Generally, vehicle operators navigate their vehicles through a pluralityof environments that involve different levels of risks for animalcollisions. The electronic device 106 may be configured to communicatevarious vehicle data associated with the vehicle 105 to the insuranceprovider 110. In the present embodiments, the vehicle data may includethe location of the vehicle 105 (e.g., GPS coordinates of the vehicle).Further, the vehicle data may alternatively or additionally include thespeed of the vehicle 105, characteristics of the vehicle 105, or variousdemographic information corresponding to the vehicle operator. It isgenerally known to those in the art that other types of information maybe communicated by the electronic device 106 (as well as by the vehicleoperator). In the present embodiments, the insurance provider 110 mayobtain the vehicle data directly from the electronic device 106 or fromanother device associated with the vehicle 105 (e.g., an on-boardcomponent).

The hardware server 125 may be coupled to a database 115 configured tostore various environment data. In some embodiments, the database 115may be configured to store and maintain a set of data that may beprovided by one or more third party entities 135 and/or may relate toresearch conducted by the insurance provider 110. In other embodiments,the third party entities 135 may store the environment data. Exemplarythird party entities may include, without limitation, databases thatstore accident records, weather reports, hunting data, animal populationdata, social media data and ecology data. The environment data may beassociated with a set of factors that influence a vehicle's level ofrisk for an animal collision. In certain embodiments, the insuranceprovider 110 may use the location of the vehicle 105 to access relevantenvironment data stored in the database 115 or retrieve relevantenvironment data from the third party entities 135, where the retrievedor accessed environment data may be relevant to the area or environmentnearby or in proximity to the location of the vehicle 105.

According to the present embodiments, the database 115 or the thirdparty entities 135 may be configured to store or determine various datathat details a plurality of various factors that may be used to assessrisks of animal collisions. For example, the environment data mayinclude ecological characteristics of the environment, characteristicsof the roadway the vehicle 105 is travelling on, a historical record ofpast animal collisions, and/or other data. In some embodiments, theecological characteristics may include various subfactors related to atleast one of the following: proximity to bodies of water, the directionof water flow for those bodies of water, the density of tree coverage,and use of land for farming crops such as corn or wheat. In otherembodiments, the roadway characteristics may also include varioussubfactors related to at least one of the following: the speed limit ofthe road, whether the road has guardrails or other fencing systems, thevisibility on the roadway, and the roadway type. Although the terms“road” and “roadway” are used herein, it should be appreciated thatanalogous terms may be envisioned for paths on which the vehicle 105 maytravel. In further embodiments, the environment data may also includeseasonal data for a particular location or environment that may alsoimpact the risk for an animal collision. As an example, the risk for ananimal collision may increase during a particular animal's huntingseason or mating season. It should be further appreciated that otherenvironment factors that may impact a vehicle's risk for an animalcollision are envisioned.

III. Exemplary Elevated Risk Communication

Referring to FIG. 2, illustrated is a signal diagram 200 associated withdetermining that a vehicle may be at an elevated risk for an animalcollision, notifying a vehicle operator of the elevated risk, and/orreceiving a collision report if an animal collision actually occurs. Inparticular, FIG. 2 includes a vehicle/customer device 206 (such as theelectronic device 106 as described with respect to FIG. 1), an insuranceprovider 210 (such as the insurance provider 110 as described withrespect to FIG. 1), and one or more third party entities 235 for storingenvironment data (such as the one or more third party entities 135 asdescribed with respect to FIG. 1). In some embodiments, the insuranceprovider 210 may contain a database that stores the environment data(such as the database 115 as described with respect to FIG. 1). Itshould be appreciated that the vehicle/customer device 206 may includeany electronic device associated with the vehicle (e.g., an on-boarddash system) and/or any electronic device associated with auser/driver/operator of the vehicle (e.g., a vehicle operator'ssmartphone, laptop, etc.). Although only one vehicle/customer device 206is depicted in FIG. 2, it should be appreciated that the insuranceprovider 210 may communicate with multiple vehicle/customer devices 206to communicate respective notifications of an elevated risk for ananimal collision and/or receive respective animal collision reports ifan animal collision occurs.

The signal diagram 200 may begin when the vehicle/customer device 206transmits (222) vehicle data, including at least a location of thevehicle, to the insurance provider 210 (or a remote server associatedwith the insurance provider). The vehicle data may further includeinformation pertaining to, without limitation, the speed of the vehicle,vehicle characteristics, and/or demographic information corresponding tothe vehicle operator. The vehicle/customer device 206 may be configuredto automatically provide the vehicle data to the insurance provider 210.Further, the vehicle/customer device 206 may provide updated vehicledata at regular or semi-regular intervals. For example, thevehicle/customer device 206 may provide updated vehicle data to theinsurance provider 210 every fifteen (15) minutes. In some embodiments,the vehicle operator may manually initiate transmission of the vehicledata to the insurance provider 210. After receiving the vehicle datafrom the vehicle/customer device 206, the insurance provider 210 mayextract at least the vehicle location from the vehicle data.

In an optional embodiment, the insurance provider 210 may retrieve (224)environment data relevant to at least the location of the vehicle fromthe third party entities 235. In another optional embodiment, theinsurance provider may access (226) environment data relevant to atleast the location of the vehicle from a database local to the insuranceprovider 210. In some embodiments, the third party entities 235 ordatabase local to the insurance provider 210 may maintain a record ofenvironment data for various locations.

The environment data relevant to the location of the vehicle may includevarious environment factors as well as different categories ofinformation for the particular environment data. In some embodiments,these environment factors (e.g., ecological characteristics) may befurther divided into a set of subfactors (e.g., proximity to water ortree coverage). In other embodiments, the environment data may include arecord of previous incidents, such as an animal collision that occurredwithin a mile of the vehicle's location within the last month. Infurther embodiments, the environment data may include factors related tothe time of year or the time of day.

An example describing the interaction of the various environment factorsis detailed as follows. Assume that a driver is driving on a countryhighway during the evening (7:00 PM) and also during moose matingseason. On the left side of the road is a stream flowing in thedirection of travel and on the right side of the road is a cornfield.Accident records for the area indicate several moose collisions haveoccurred on this particular stretch of road during the last month. Inanalyzing the environment data, the system may determine that duringmating season, moose are more likely to be mobile in the evening andthat moose are more likely to find mates near running water. The systemmay also determine the moose are likely to eat corn before looking for amate. As such, the system may determine that there is an elevated riskfor a moose collision due to the likelihood of a moose, while lookingfor a mate, crossing the road from the cornfield to the stream (i.e.,crossing from right side of the road to the left side of the road).Additionally, the system may further determine that the time of dayindicates reduced visibility due to either darkness or sun glare, whichmay further increase the risk of collision. Still further, the systemmay determine that the country highway contains no roadside barriers toprevent animal crossings and may adjust the level of risk accordingly.The system may increase the determined risk even further based upon thehistorical record of past moose collisions on this stretch of the road.While the above example describes the system analyzing the environmentrisk factors for the risk of a moose collision, it should be appreciatedthat the system may be capable of analyzing similar environment data tocalculate the risk of collision with a plurality of other variousanimals. For example, in addition to determining that a vehicle is at anelevated risk for a moose collision, it may also determine that possumare generally at rest around 7:00 PM, and therefore there is a low riskfor a possum collision.

The insurance provider 210 may assess (228) a level of risk for thevehicle to experience an animal collision based upon the localizedenvironment data. In assessing the level of risk for the vehicle toexperience an animal collision, the insurance provider 210 maysupplement the localized environment data with vehicle data other thanthe vehicle's location. Similar to the environment data, thenon-location vehicle data may include a set of vehicle factors that mayor may not be further divided into subfactors. For example, thenon-location vehicle data may include various vehicle characteristicsthat may be divided into exemplary subfactors such as age of thevehicle, time since last brake replacement, vehicle maintenanceinformation, and/or other information.

An example describing the interaction of the various environment factorsis detailed as follows. Assume that an elderly person is driving a carmanufactured in 1982. The mileage on the car indicates that a brakereplacement should have occurred 5000 miles ago. The system maydetermine that the driver is at an elevated risk for an animal collisiondue to his advanced age since the driver has reduced reaction times,thus impairing his ability to avoid a collision. Additionally oralternatively, the system may automatically determine that the vehiclehas been driven a substantial amount of time or miles in a given day bya single operator, and thus that operator may be fatigued or have alower level of alertness. The system may further determine that the ageof the vehicle and its need for new brakes reduces the ability of thevehicle to come to a stop quickly further elevating the determined levelof risk.

In assessing the level of risk for the vehicle to experience an animalcollision, the insurance provider 210 may implement one or more machinelearning algorithms. In particular, the insurance provider 210 may use amachine learning algorithm to assign weights to one or more environmentfactors or subfactors. The machine learning algorithm may calculate thespecific weight for each factor or subfactor using a formula to assistin various statistical analyses (e.g., linear/logistic regression,generalized linear models, neural networks, Bayesian networks, Gaussianregression, ensemble methods, and/or others). In some cases,implementing and comparing an ensemble of different machine learningalgorithms may provide better predictive performance than individualmachine learning algorithms. The insurance provider 210 may combine theset of environment factors (and any subfactors associated therewith) andvehicle factors (and any subfactors associated therewith) to calculatean overall level of risk for the vehicle to experience an animalcollision. In some embodiments, the overall level of risk may be aquantitative measurement (e.g., a 10% chance of collision). In otherembodiments, the overall level of risk may be a qualitative measurement(e.g., low, medium, high, etc.).

The insurance provider 210 may determine (230) if there is an elevatedlevel of risk for an animal collision based on the assessment of therisk level. In some embodiments, the insurance provider 210 can comparethe calculated overall level of risk to a threshold level of acceptablerisk. For example, the threshold level of acceptable risk may be 10% andthe calculated level of risk may be 15%. In some cases, the level ofrisk may be represented by a number or measurement instead of apercentage. In other cases, if the risk levels are qualitative, theinsurance provider 210 may deem there to be an elevated risk if theoverall level of risk is anything greater than “low.” If the insuranceprovider determines that there is not an elevated level of risk (“NO”),the insurance provider 210 may return to await the arrival of updatedvehicle data sent from the vehicle/customer device 206. In contrast, ifthe insurance provider determines that there is an elevated level ofrisk (“YES”), the insurance provider 210 may generate (232) anotification indicating that the vehicle is at an elevated risk for ananimal collision.

According to embodiments, the generated notification may indicate aparticular animal for which the vehicle is at the elevated risk ofhaving a collision. The generated notification may also includeinformation that alerts the vehicle operator to pay extra attention tothe possibility that the particular animal may interfere with the pathof the vehicle. The generated notification may further includeinformation indicating the source of the elevated risk for an animalcollision. For example, the vehicle operator may be driving during deerhunting season and in a known forested hunting range located on theright side of the road. In such an example, the alert may indicate thatthe vehicle operator should pay extra attention for deer that may enterthe roadway from the right side of the road. In some embodiments, in aneffort to reduce vehicle operator distraction, the notification may alsoinclude an obvious indication that a warning is contained within thenotification, such as a flashing yellow triangle.

In some embodiments, the notification may be a text-based warningdescribing the risks to the vehicle operator. In other embodiments, thenotification may be an image indicating that the vehicle is at anelevated risk for an animal collision. In still other embodiments, thenotification may be an audio alert capable of dictating the notificationto the vehicle operator. Moreover, in the present embodiments, thenotification may trigger haptic feedback to the vehicle operator, suchas causing the vehicle/customer device 206 to vibrate or causing thesteering mechanism of the vehicle to vibrate. The insurance provider 210may choose to generate a notification capable of utilizing any onemedium or combination of media thereof for notification, including butnot limited to text, image, audio, or haptic feedback. It is should beappreciated that other techniques for generating a notification, as wellas other types of notifications, are envisioned, such as a video,augmented reality elements, and/or the like.

After the insurance provider 210 generates the notification, theinsurance provider 210 may communicate (234) the notification to thevehicle/customer device 206. It should be appreciated that variouschannels of communication for communicating the notification areenvisioned, where the channel of communication may vary based on thetype of notification. After receiving the notification, thevehicle/customer device may notify (236) the customer by communicating,annunciating, or otherwise presenting the notification. It should beappreciated that various channels for notifying the customer areenvisioned. For example, a text message (SMS) notification may becommunicated to the vehicle/customer device 206 via an availablecellular network, and the vehicle/customer device 206 may display theSMS notification. In another example, an audio alert may be communicatedvia a satellite to an antenna coupled with a vehicle infotainmentconsole, and the corresponding vehicle/customer device 206 may notify avehicle operator by automatically annunciating the audio alert.

Although the notifications are meant to warn vehicle operators ofelevated risks for animal collisions before an animal collision occurs,there still may be instances in which vehicles experience animalcollisions. If, at some time after the notification is communicated tothe vehicle operator, the vehicle collides (238) with an animal (“YES”),the vehicle operator may fill out and transmit (240) an animal collisionreport to the insurance provider 210. In some cases, the vehicleoperator may use the vehicle/customer device 206 (or another device) tofill out and transmit the animal collision report. It should beappreciated that the vehicle operator may use the vehicle/customerdevice 206 to fill out and transmit an animal collision report even ifthe vehicle/customer device does not receive a notification. If thevehicle does not collide with an animal (“NO”), then thevehicle/customer device 206 may continue to transmit vehicle data to theinsurance provider 210.

The vehicle/customer device 206 may enable the vehicle operator (oranother user) to manually input information pertaining to the animalcollision. According to the present embodiments, the information mayinclude the date and time of the collision, the location of thecollision, the type of animal involved in the collision, and/or anyother details or information associated with the animal collision.Further, at least some of the information included in the animalcollision report may be generated automatically by the vehicle/customerdevice 206. In some embodiments, the vehicle operator may choose tosupplement the animal collision report with photographs or video of thecollision or the area surrounding the collision.

After receiving the animal collision report, the insurance provider 210may extract any information included in the animal collision report andassociate the information with corresponding environment factors orsubfactors, and/or any vehicle factors or subfactors. The insuranceprovider 210 may update (242) the machine learning algorithm accordingto any extracted information. In particular, the insurance provider 210may update the specific weights within the machine learning algorithmbased upon the extracted data associated with each environment factor orsubfactor and/or vehicle factor or subfactor. In some cases, a factor orsubfactor included in the animal collision report may be weighted higherafter the machine learning algorithm updates the weights. Conversely, afactor or subfactor that was absent from the animal collision report maybe weighted less after the machine learning algorithm updates theweights. It should be appreciated that factors or subfactors may existwherein the absence of the factor or subfactor may weight the factor orsubfactor higher and, correspondingly, the weight of the factor orsubfactor may be lowered by the factor or subfactor's presence. Forexample, when updating the weights of the factors or subfactors, if thecollision occurred near a dense forest, the machine learning algorithmmay adjust the environment factors or subfactors associated with treedensity higher. Further, the insurance provider 210 may store thelocation and date of the animal collision in the database local to theinsurance provider 210.

The insurance provider 210 may then pre-populate an insurance claim 243so that it is easier for the insured customer to complete the insuranceclaim after the collision with the animal. This may be accomplished byextracting known data about the customer and the insured vehicle fromone or more existing databases as well as the environment and collisiondata received from the vehicle/customer device 206.

IV. Exemplary User Interfaces

FIG. 3 illustrates an exemplary interface 350 that notifies a customerthat the vehicle is at an elevated risk for an animal collision. Asdiscussed herein, a vehicle/customer device may be configured to displaythe notification, where the notification is received from an insuranceprovider (or a vehicle control system). As illustrated in FIG. 3, theinterface 350 may provides an indication of an alert (i.e., a warningsymbol) and a description of the nature of the elevated risk (“A boarcollision has occurred in the area recently. Be on alert!”). Althoughnot illustrated in FIG. 3, it should be noted that interface 350 mayinclude additional information associated with the elevated risk, suchas a specific direction from which an animal is likely to cross thevehicle's path.

FIG. 4 illustrates an exemplary interface 450 indicating an exampleanimal collision report form. According to the present embodiments, theinterface 450 may include selections, input boxes, or the like thatenable the user to input data associated with an animal collision. Asillustrated in FIG. 4, the data may include a date 452 (“01/01”), a time454 (“11:45 am”), a type of animal (“Elk”) 456, and other details aboutthe collision 458 (“I hit an elk . . . ”). At least some of thisinformation may be automatically prepopulated by the vehicle/customerdevice. Although not illustrated in FIG. 4, manually entered orautomatically generated location data may also be included in an animalcollision report. Some embodiments may also enable a user to attachimages 460 (img01.jpg) or videos to the animal collision report. Theimages or videos may be taken by mobile devices associated with theoperator, and/or by vehicle mounted cameras.

V. Exemplary Method of Risk Assessment

Referring to FIG. 5, depicted is a block diagram of an example method500 for assessing a risk for an animal collision and communicating anotification of the elevated risk. The method 500 may be facilitatedbetween the insurance provider 110 as depicted in FIG. 1 and a customerassociated with the vehicle. The customer may use any type of electronicor computing device (such as the electronic device 106 as depicted inFIG. 1, and/or a smart vehicle controller associated with the vehicle105) to provide vehicle data, receive a notification, and/or transmit ananimal collision report.

The insurance provider may receive, from the customer, vehicle dataassociated with the vehicle including at least information indicting thevehicle's location (block 505). The vehicle data may additionallyinclude various vehicle factors and subfactors that may impact thevehicle's risk for an animal collision. Based upon at least the locationof the vehicle, the insurance provider may access environment dataassociated with the location as well as any other relevant data includedin the received vehicle data (block 510). The environment data mayinclude a plurality of environment factors and subfactors that mayimpact the vehicle's risk for an animal collision. The insuranceprovider may assess the overall level of risk of the vehicle for ananimal collision based upon the plurality of vehicle factors andsubfactors and/or the environment factors and subfactors (block 520). Inthe present embodiments, the insurance provider may assess the overalllevel of risk according to one or more machine learning algorithms,whereby the machine learning algorithms can assign weights to anyvehicle factors or subfactors and/or any environment factors and/orsubfactors.

The insurance provider may then determine whether there is an elevatedrisk for an animal collision, such as by determining whether the overalllevel of risk exceeds a threshold value (block 525). The threshold valuemay represent the maximum level of risk that an insurance provider iswilling to take on without notifying the customer. If the insuranceprovider determines there is not an elevated risk for an animalcollision (“NO”), processing may return to block 505 or proceed to otherfunctionality. If the insurance provider determines that there is anelevated risk for an animal collision (“YES”), processing may proceedwith the insurance provider generating a notification that indicates anelevated risk for an animal collision (block 530). The insuranceprovider may subsequently communicate the notification to the customer(block 535). As discussed herein, the insurance provider may utilize aplurality of communication channels for communicating the notification,such as to either a mobile device associated with the vehicle driver orto a control or communications system of the vehicle.

Subsequent to the insurance provider communicating the notification,there still may be a situation in which the vehicle collides with ananimal. In this case, the customer may fill out an animal collisionreport and communicate the animal collision report to the insuranceprovider. Accordingly, the insurance provider may receive the animalcollision report from the customer (block 540). If an animal collisionreport is received, the insurance provider may update the machinelearning algorithm based upon the details of the animal collision report(block 545). In particular, the insurance provider may update anyweights associated with any vehicle factors and subfactors, and/orenvironment (including geographical, road, weather, visibility,time-related, and/or other) factors and subfactors, that are present orabsent in the animal collision report.

VI. Exemplary Method of Processing Collision Risk

In one aspect, a computer-implemented method of processing vehiclecollision risk information may be provided. The method may include: (1)receiving, at a hardware server, vehicle data indicating at least alocation of a vehicle; (2) accessing, by a processor, environment dataassociated with the location of the vehicle; (3) based on theenvironment data, determining, by the processor, that the vehicle is atan elevated risk for an animal collision; (4) generating, by theprocessor, a notification indicating the elevated risk; and/or (5)communicating, via a communications network, the notification to thevehicle.

Communicating the notification to the vehicle may include communicatingthe notification to at least one of an onboard computer of the vehicleand an electronic device associated with an operator of the vehicle.Receiving the vehicle data may include receiving at least one of a speedof the vehicle, vehicle characteristics, and demographic informationassociated with an operator of the vehicle. Accessing the environmentdata may include accessing at least one of: a historical record ofaccidents, ecological characteristics, and/or roadway characteristics.Determining that the vehicle is at the elevated risk may includedetermining, from the environment data, that a previous accident hasoccurred at or near the location of the vehicle. The environment datamay include a first environment factor having a first specific weightand a second environment factor having a second specific weight, anddetermining that the vehicle is at an elevated risk may includecalculating an overall risk based on combining the first environmentfactor and the second environment factor.

Determining that the vehicle is at the elevated risk may include (1)identifying at least one of a time of day and a time of year; and (2)determining, from a portion of the environment data corresponding to theat least one of the time of day and the time of year, that the vehicleis at the elevated risk. The method may further include receiving, atthe hardware server, an animal collision report indicating that thevehicle collided with an animal. Determining that the vehicle is at theelevated risk includes executing a machine learning algorithm. Themethod may also include updating the machine learning algorithmaccording to the animal collision report. The method may includeadditional, fewer, or alternate actions, including those discussedelsewhere herein.

VII. Exemplary System for Processing Collision Risk

In one aspect, a system for processing vehicle collision riskinformation may be provided. The system may include (1) a communicationmodule adapted to communicate data; (2) a memory adapted to storenon-transitory computer executable instructions; (3) a hardware serverto store environment data; and/or (4) a processor adapted to interfacewith the communication module. The processor may be configured toexecute the non-transitory computer executable instructions to cause theprocessor to: (a) receive, via the communication module, vehicle dataindicating at least a location of a vehicle, (b) access, via theprocessor, at least a portion of the environment data associated withthe location of the vehicle, (c) based on the environment data,determine, via the processor, that the vehicle is at an elevated riskfor an animal collision, (d) generate, via the processor, a notificationindicating the elevated risk, and (e) communicate, via the communicationmodule, the notification to the vehicle.

To communicate the notification to the vehicle, the communication modulemay be configured to: communicate the notification to at least one of anonboard computer of the vehicle and an electronic device associated withan operator of the vehicle. To receive the vehicle data, the hardwareserver may be configured to: receive at least one of a speed of thevehicle, vehicle characteristics, the location of the vehicle, anddemographic information associated with an operator of the vehicle. Tofacilitate accessing the environment data, the processor may beconfigured to execute the non-transitory computer executableinstructions to cause the processor to access at least one of: ahistorical record of accidents, ecological characteristics, and roadwaycharacteristics. To facilitate determining that the vehicle is at theelevated risk, the processor is configured to determine, from theenvironment data, that a previous accident has occurred at or near thelocation of the vehicle. The environment data may include a firstenvironment factor associated with a specific weight and a secondenvironment factor associated with a specific weight, and whereindetermining that the vehicle is at an elevated risk may includecalculating, via the processor, an overall risk based on combining thefirst environment factor and the second environment factor.

To facilitate determining that the vehicle is at the elevated risk, theprocessor may be configured to identify at least one of a time of dayand a time of year; and determine, from a portion of the environmentdata corresponding to the at least one of the time of day and the timeof year, that the vehicle is at the elevated risk. The processor may befurther configured to receive an animal collision report indicating thatthe vehicle collided with an animal. To facilitate determining that thevehicle is at the elevated risk the processor may be configured toexecute a machine learning algorithm. The processor may be furtherconfigured to update the machine learning algorithm according to theanimal collision report. The system may include additional, less, oralternate components and functionality, including that discussedelsewhere herein.

VIII. Exemplary Computer-Implemented Methods

In one aspect, a computer-implemented method of avoiding or reducing thelikelihood of a vehicle-animal collision may be provided. The method mayinclude (a) identifying high risk areas of vehicle-animal collision, thehigh risk areas being identified based upon one or more of: (1)location, (2) time of day, and (3) day of year; (b) monitoring orotherwise identifying a location of a mobile device; and/or (c) when acurrent location of the mobile device matches the location of the highrisk area, if the current time of day and current day of year match thetime of day and day of year associated with the high risk area, causingthe mobile device to issue an alert or warning to the user. Additionallyor alternatively, other characteristics associated with the high riskarea may be matched or compared. The method may include additional,fewer, or alternate actions, including those discussed elsewhere herein.

In another aspect, a computer-implemented method of avoiding avehicle-animal collision may be provided. The method may include (a)identifying high risk areas of vehicle-animal collision; (b) monitoringor identifying a location of a mobile device; and/or (c) when a currentlocation of the mobile device is approaching the high risk area, if oneor more other parameters associated with the high risk area match,causing the mobile device to issue an alert or warning to the user. Theone or more other parameters associated with the high risk area that maybe matched to cause an alert or warning to issue may be associated withtime of day; day of year; weather; harvest; field; geography; wildlifepreserve; animal-related information (such as animal movement or matinginformation); and/or other information discussed elsewhere herein. Themethod may include additional, fewer, or alternate actions, includingthose discussed elsewhere herein.

In another aspect, a computer-implemented method of issuing an alertassociated with a vehicle-animal collision may be provided. The methodmay include (i) predicting a (1) geographical scope, and (2) a temporalscope of an area at high risk of being associated with a vehicle-animalcollision; (ii) monitoring or identifying a current location of a mobiledevice; (iii) monitoring or identifying a current time; and/or (iv) when(a) the current location of the mobile device matches or falls withinthe geographical scope of the area at high risk, and/or (b) the currenttime matches or falls within the temporal scope of the area at highrisk, causing the mobile device to issue an alert to the user. Thegeographical scope may include or be associated with an area defined bylatitude/longitude coordinates, mile markers on a stretch of highway,county lines, distance information, etc. The geographical scope may alsobe associated with terrain information (hills, forest, open land, farmland, prairie, river, creek, field, and/or other geographical-relatedcharacteristics). For example, the terrain information may facilitatedefining the size or physical boundaries of the geographical scope ofthe high risk area. The temporal scope may include or be associated withtime of day, seasonal, day of month, day of year, month, and/or othertime-related information. The method may include additional, fewer, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer-implemented method of issuing an alertassociated with a likelihood of a vehicle-animal collision may beprovided. The method may include, via one or more processors, (i)predicting (1) a geographical scope, (2) a temporal scope, and/or (3) aseasonal scope of an area at high risk of being associated with avehicle-animal collision; (ii) monitoring or identifying a currentlocation of a mobile device; (iii) monitoring or identifying a currenttime of day; (iv) monitoring or identifying a current time of year;and/or (v) when (a) the current location of the mobile device matches orfalls within the geographical scope of the area at high risk, (b) thecurrent time matches, falls within, or coincides with the temporal scopeof the area at high risk, and/or (c) the current time of year matches,falls within, or coincides with the seasonal scope of the area at highrisk causing the mobile device to issue an alert to the user. Thetemporal scope may include or be associated with, for example, time ofday, day/night, sunrise/sunset, dusk/dawn, and other information. Theseasonal scope may include or be associated with month or spring,summer, fall, and winter information; harvest/planting information;weather information, and/or other information. Other characteristics ofthe area at high risk of being associated with a vehicle-animalcollision, such as those discussed elsewhere herein, may also be matchedor compared to current conditions to further enhance the accuracy of thealert being issued.

Predicting (1) the geographical scope, (2) the temporal scope, and (3)the seasonal scope of the area at high risk of being associated with avehicle-animal collision may include analyzing a database of actualvehicle collisions involving vehicles and animals. Alternatively oradditionally, predicting the area at high risk of being associated witha vehicle-animal collision further comprises analyzing automobileaccidents, driver characteristics, animal tendencies, weather, calendar,time of day, geographical information. The method may further include(a) the processor determining whether or not the vehicle is movingbefore issuing the alert or warning, and/or (b) include adjusting aninsurance premium, discounts, or reward based upon animal collisionavoidance functionality. The method may include additional, fewer, oralternate actions, including those discussed elsewhere herein.

In another aspect, a computer-implemented method of issuing an alertassociated with a likelihood of a vehicle-animal collision occurring maybe provided. The method may include (i) creating a database of high riskareas where vehicle-animal collision are likely to occur, the high riskareas may comprise or be defined by (1) a geographical scope, (2) atemporal scope, and/or (3) a seasonal scope associated with an area athigh risk of being associated with a vehicle-animal collision; and/or(ii) when (a) the current location of the mobile device approaches orfalls within the geographical scope of a high risk area, (b) the currenttime coincides with the temporal scope of the high risk area, and/or (c)the current time of year coincides with the seasonal scope of the highrisk area causing the mobile device to issue an alert or warning to theuser. Other characteristics of the high risk area may also be comparedto current conditions in determining whether to issue an alert. Thealert or warning may be an audible, visual, vibrational, haptic, orother type of alert. As an example, the frequency that a light flashesor a sound beeps, or the audible level of the beep, may be dependent onthe level of the likelihood of a vehicle-animal collision occurring. Themethod may include additional, fewer, or alternate actions, includingthose discussed elsewhere herein.

The methods discussed above and herein may further include takinginsurance-related actions based upon the collision avoidancefunctionality detailed herein. The method may include adjustinginsurance rates, premiums, discounts, and/or rewards. For instance, theremote server associated with an insurance provider that may generatecollision avoidance alerts or warnings may also calculatecustomer-specific insurance premiums, rates, rewards, points, discounts,and/or other customer-specific items. The customer-specificinsurance-related items may be calculated based upon the amount and/ortype of animal collision functionality that a customer's mobile deviceor vehicle is equipped with.

The amount or percentage of time that a vehicle operator employs animalcollision avoidance functionality may be determined from vehicle usagedata collected (with the vehicle operator's permission). Thecustomer-specific insurance-related items, such as premiums ordiscounts, may be adjusted according to the percentage of time that thevehicle is operated with the collision avoidance functionality beingused. For instance, if an insured operates a vehicle with animalcollision avoidance functionality employed for all or a substantialamount of the time, that insured may receive a discount, or reward fromthe insurance provider.

IX. Exemplary Mobile Devices

In one aspect, a mobile device configured to facilitate the avoidance ofa vehicle-animal collision may be provided. The mobile device mayinclude a memory storing information associated with high risk areas ofvehicle-animal collision; and/or a processor configured to: (a) monitoror identify a GPS location of the mobile device; (b) determine that themobile device is within a moving vehicle (such as via speed sensor, ormonitoring speed of movement via GPS coordinates); and/or (c) predict oridentify that the mobile device is within or approaching a high riskarea based upon at least the GPS location of the mobile device. When theprocessor determines that the mobile device is (1) within a movingvehicle, (2) is within or approaching a high risk area, and/or (3)verifies at least one additional current condition corresponds to acharacteristic of the high risk area (such as time of day; day of year;geography; weather; traffic; or other characteristics discussed herein),the mobile device may issue an alert or warning to the user to indicatethat the vehicle is within or approaching an area of high risk ofvehicle-animal collision. The mobile device may include additional,fewer, or alternate functionality, including that discussed elsewhereherein.

In another aspect, a mobile device (or smart vehicle communications andcontrol system) configured to issue an alert associated with thelikelihood of a vehicle-animal collision may be provided. The mobiledevice (or smart vehicle communications and control system) may include(i) a memory storing a database of high risk areas where vehicle-animalcollision are likely to occur, the high risk areas being defined in thememory by at least fields related to (1) a geographical scope, (2) atemporal scope, and/or (3) a seasonal scope of an area at high risk ofbeing associated with a vehicle-animal collision; and/or (ii) aprocessor configured to issue an alert or warning to the user when (a)the current location of the mobile device (or smart vehiclecommunications and control system) approaches or falls within thegeographical scope of a high risk area, (b) the current time coincideswith the temporal scope of the high risk area, and/or (c) the currenttime of year coincides with the seasonal scope of the high risk area.The mobile device (or smart vehicle communications and control system)may include additional, fewer, or alternate functionality, includingthat discussed elsewhere herein.

In another aspect, a mobile device (or smart vehicle communications andcontrol system) configured to issue an alert associated with thelikelihood of a vehicle-animal collision occurring may be provided. Themobile device (or smart vehicle communications and control system) mayinclude (i) a processor configured to monitor or otherwise identify acurrent location of the mobile device (or smart vehicle communicationsand control system); (ii) a transceiver configured to transmit thecurrent location of the mobile device (or smart vehicle communicationsand control system) to a remote server or processor; (iii) thetransceiver further configured to receive a high risk message from theremote server or processor indicating the mobile device (or smartvehicle mounted communications and control system, i.e., the vehicle) isapproaching or within an area at high risk of vehicle-animal collision;and/or (iv) when a high risk message is received by the transceiver atthe mobile device (or smart vehicle communications and control system),the processor directs the mobile device (or smart vehicle communicationsand control system) to issue an alert or warning to the user indicativethat the user is about to or has entered the area at high risk ofvehicle-animal collision. The mobile device (or smart vehiclecommunications and control system) may include additional, fewer, oralternate functionality, including that discussed elsewhere herein.

X. Exemplary Collision Avoidance Functionality

In one aspect, a mobile device configured to facilitate the avoidanceof, or reducing the likelihood of, a vehicle-animal collision may beprovided. The mobile device may include a means for storing informationassociated with high risk areas of vehicle-animal collision; and/or (a)means for monitoring or identifying a GPS location of the mobile device;(b) means for determining that the mobile device is within a movingvehicle (such as via speed sensor, or monitoring speed of movement viaGPS coordinates); and/or (c) means for identifying that the mobiledevice is within or approaching a high risk area based upon at least theGPS location of the mobile device. When the mobile device determinesthat the mobile device is (1) within a moving vehicle, (2) is within orapproaching a high risk area, and/or (3) verifies at least oneadditional current condition corresponds to a characteristic of the highrisk area (such as time of day; day of year; geography, weather;traffic; or other characteristics discussed herein), the mobile deviceactivates a means for issuing an alert or warning to the user toindicate that the vehicle is within or approaching an area of high riskof vehicle-animal collision. The mobile device may include one or moreprocessors, memory units, a combination of processor(s) and memoryunit(s), applications, non-transitory computer instructions, and/orother components that provide for or implement the “means for”functionality noted above. The mobile device may include additional,fewer, or alternate functionality, including that discussed elsewhereherein.

In another aspect, a mobile device (or smart vehicle communications andcontrol system) configured to issue an alert associated with thelikelihood of a vehicle-animal collision may be provided. The mobiledevice (or smart vehicle system) may include (i) means for storing highrisk areas where vehicle-animal collision are likely to occur, the highrisk areas may be defined by (1) a geographical scope, (2) a temporalscope, and/or (3) a seasonal scope of an area at high risk of beingassociated with a vehicle-animal collision; and (ii) means for issuingan alert or warning to the user when (a) the current location of themobile device (or smart vehicle) approaches or falls within thegeographical scope of a high risk area, (b) the current time coincideswith the temporal scope of the high risk area, and/or (c) the currenttime of year coincides with the seasonal scope of the high risk area.The mobile device (or smart vehicle system) may include means forcomparing current mobile device (or smart vehicle) conditions withcharacteristics associated with the high risk areas. The mobile device(or smart vehicle system) may include one or more processors, memoryunits, a combination of processor(s) and memory unit(s), applications,non-transitory computer instructions, and/or other components thatprovide for or implement the “means for” functionality noted above. Themobile device (or smart vehicle system) may include additional, fewer,or alternate functionality, including that discussed elsewhere herein.

In another aspect, a mobile device (or smart vehicle system) configuredto issue an alert associated with the likelihood of a vehicle-animalcollision occurring may be provided. The mobile device (or smart vehiclesystem) may include (i) means for monitoring a current location of themobile device (or smart vehicle); (ii) transmitting means for wirelesslytransmitting the current location of the mobile device (or smartvehicle) to a remote server or processor; (iii) receiving means forwirelessly receiving a high risk message from the remote server orprocessor indicating the mobile device (or smart vehicle) is approachingor within an area at high risk of vehicle-animal collision; and/or (iv)when a high risk message is received by the transceiver at the mobiledevice (or smart vehicle system), means for directing the mobile device(or smart vehicle system) to issue an alert or warning to the userindicative that the user is about or has entered the area at high riskof vehicle-animal collision. The mobile device (or smart vehicle system)may include one or more processors, memory units, a combination ofprocessor(s) and memory unit(s), applications, non-transitory computerinstructions, and/or other components that provides for or implementsthe “means for” functionality noted above. Additionally oralternatively, the transmitting means and/or the receiving means mayinclude a transmitter, receiver, transceiver, processor, memory, and/orother components that provide wireless communication functionality. Themobile device (or smart vehicle system) may include additional, fewer,or alternate functionality, including that discussed elsewhere herein.

XI. Exemplary Remote Server Functionality

In another aspect, a remote server configured to wirelessly issue alertsassociated with the likelihood of animal collision to remote mobiledevices may be provided. The server may include (i) a memory containingvehicle accident information involving vehicles and animals; (ii) atransceiver configured to receive a location of remote mobile device (orsmart vehicle system) that is transmitted from the remote mobile device(or smart vehicle system); and (iii) a processor configured to determinewhen the remote mobile device (or smart vehicle) is approaching orwithin a high risk area, the high risk area being an area associatedwith a high risk for traveling vehicles to collide with animals, whereinwhen the processor determines the remote mobile device (or smartvehicle) is approaching or is within the high risk area, the processortransmits an alert message via the transceiver to the remote mobiledevice (or smart vehicle system) to alert a user associated with theremote mobile device (or smart vehicle) of the high risk area. Theserver may include additional, fewer, or alternate functionality,including that discussed elsewhere herein.

In another aspect, a remote server configured to wirelessly issue alertsassociated with the likelihood of animal collision to remote mobiledevices (or smart vehicle systems) may be provided. The server mayinclude (i) means for storing vehicle accident information involvingvehicles and animals; (ii) transceiver means for wirelessly receiving alocation of remote mobile device (or smart vehicle system) that istransmitted from the remote mobile device (or smart vehicle system); and(iii) means for determining when the remote mobile device (or smartvehicle) is approaching or within a high risk area, the high risk areabeing an area associated with a high risk for traveling vehicles tocollide with animals. When it is determined that the remote mobiledevice (or smart vehicle) is approaching or is within the high riskarea, the transceiver means transmits an alert message via thetransceiver to the remote mobile device (or smart vehicle system) toalert a user associated with the remote mobile device (or smart vehicle)of the high risk area. The remote server may include one or moreprocessors, memory units, a combination of processor(s) and memoryunit(s), applications, non-transitory computer instructions, wirelesstransceivers, receivers, transmitters, and/or other components thatprovide for or implements the “means for” functionality noted above. Theserver may include additional, fewer, or alternate functionality,including that discussed elsewhere herein.

For instance, a remote server located at an insurance provider locationmay calculate adjustments for insurance premiums, rates, discounts,points, or rewards based upon the amount of time that a specificcustomer employs the collision avoidance functionality, as discussedelsewhere herein. The remote server may collect data indicating the typeand/or amount of usage of collision avoidance functionality utilized bythe insured. After which, the remote server may calculate insurancesavings for that insured based upon the type and/or amount of usage ofcollision avoidance functionality.

XII. Exemplary Server

FIG. 6 illustrates a diagram of an exemplary hardware server 625 (suchas the hardware server 125 as discussed with respect to FIG. 1) in whichthe functionalities as discussed herein may be implemented. It should beappreciated that the hardware server 625 may be associated with aninsurance provider, as discussed herein.

The hardware server 625 may include a processor 622 as well as a memory678. The memory 678 may store an operating system 679 capable offacilitating the functionalities as described herein. The hardwareserver 625 may also store a set of applications 675 (i.e, machinereadable instructions). For example, one of the set of applications 675may be a machine learning algorithm 684 configured to calculate avehicle's overall level of risk for an animal collision. It should beappreciated that other applications are envisioned.

The processor 622 may interface with the memory 678 to execute theoperating system 679 and the set of applications 675. According to someembodiments, the memory 678 may also include environment data 680 thatincludes information related to environment factors that can impact avehicle's level of risk for an animal collision. The machine learningalgorithm 684 may access the environment data 680 to calculate anoverall level of risk. The memory 678 may include one or more forms ofvolatile and/or non-volatile, fixed and/or removable memory, such asread-only memory (ROM), electronic programmable read-only memory(EPROM), random access memory (RAM), erasable electronic programmableread-only memory (EEPROM), and/or other hard drives, flash memory,MicroSD cards, and others.

The hardware server 625 may further include a communication module 677configured to communicate data via one or more networks 620. Accordingto some embodiments, the communication module 677 can include one ormore transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 676. For example, the communication module 677 may send,via the network 620, a notification to a customer to alert the customerthat the vehicle may be at an elevated risk for an animal collision. Theprocessing server 625 may further include a user interface 681configured to present information to a user and/or receive inputs fromthe user. As shown in FIG. 6, the user interface 681 may include adisplay screen 682 and I/O components 683 (e.g., ports, capacitive orresistive touch sensitive input panels, keys, buttons, lights, LEDs,speakers, microphones). According to the present embodiments, the usermay access the hardware server 625 via the user interface 681 to processupdate the environment data and/or perform other functions. In someembodiments, the hardware server 625 may perform the functionalities asdiscussed herein as part of a “cloud” network or can otherwisecommunicate with other hardware or software components within the cloudto send, retrieve, or otherwise analyze data.

The hardware server 625 may be a local or remote server. For instance,the hardware server 625 may be a remote server, such as a remote locatedserver associated with the insurance provider. Additionally oralternatively, the hardware server 625 may be located on the vehicle andcomprise part of the vehicle's communication and/or control system.Other servers may be used.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code is adapted to beexecuted by the processor 622 (e.g., working in connection with theoperating system 679) to facilitate the functions as described herein.In this regard, the program code may be implemented in any desiredlanguage, and may be implemented as machine code, assembly code, bytecode, interpretable source code or the like (e.g., via Python, or otherlanguages, such as C, C++, Java, Actionscript, Objective-C, Javascript,CSS, XML). In some embodiments, the computer program product may be partof a cloud network of resources.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

The term “insurance policy,” as used herein, generally refers to acontract between an insurer and an insured. In exchange for paymentsfrom the insured, the insurer pays for damages to the insured which arecaused by covered perils, acts or events as specified by the language ofthe insurance policy. The payments from the insured are generallyreferred to as “premiums,” and typically are paid on behalf of theinsured upon purchase of the insurance policy or over time at periodicintervals. The amount of the damages payment is generally referred to asa “coverage amount” or a “face amount” of the insurance policy. Aninsurance policy may remain (or have a status or state of) “in-force”while premium payments are made during the term or length of coverage ofthe policy as indicated in the policy. An insurance policy may “lapse”(or have a status or state of “lapsed”), for example, when theparameters of the insurance policy have expired, when premium paymentsare not being paid, when a cash value of a policy falls below an amountspecified in the policy (e.g., for variable life or universal lifeinsurance policies), or if the insured or the insurer cancels thepolicy.

The terms “insurer,” “insuring party,” and “insurance provider” are usedinterchangeably herein to generally refer to a party or entity (e.g., abusiness or other organizational entity) that provides insuranceproducts, e.g., by offering and issuing insurance policies. Typically,but not necessarily, an insurance provider may be an insurance company.

Although the embodiments discussed herein relate to vehicle orautomobile insurance policies, it should be appreciated that aninsurance provider may offer or provide one or more different types ofinsurance policies. Other types of insurance policies may include, forexample, homeowners insurance; condominium owner insurance; renter'sinsurance; life insurance (e.g., whole-life, universal, variable, term);health insurance; disability insurance; long-term care insurance;annuities; business insurance (e.g., property, liability, commercialauto, workers compensation, professional and specialty liability, inlandmarine and mobile property, surety and fidelity bonds); boat insurance;insurance for catastrophic events such as flood, fire, volcano damageand the like; motorcycle insurance; farm and ranch insurance; personalarticle insurance; personal liability insurance; personal umbrellainsurance; community organization insurance (e.g., for associations,religious organizations, cooperatives); and other types of insuranceproducts. In embodiments as described herein, the insurance providersprocess claims related to insurance policies that cover one or moreproperties (e.g., homes, automobiles, personal articles), althoughprocessing other insurance policies is also envisioned.

The terms “insured,” “insured party,” “policyholder,” “customer,”“claimant,” and “potential claimant” are used interchangeably herein torefer to a person, party, or entity (e.g., a business or otherorganizational entity) that is covered by the insurance policy, e.g.,whose insured article or entity (e.g., property, life, health, auto,home, business) is covered by the policy. A “guarantor,” as used herein,generally refers to a person, party or entity that is responsible forpayment of the insurance premiums. The guarantor may or may not be thesame party as the insured, such as in situations when a guarantor haspower of attorney for the insured. An “annuitant,” as referred toherein, generally refers to a person, party or entity that is entitledto receive benefits from an annuity insurance product offered by theinsuring party. The annuitant may or may not be the same party as theguarantor.

Typically, a person or customer (or an agent of the person or customer)of an insurance provider fills out an application for an insurancepolicy. In some cases, the data for an application may be automaticallydetermined or already associated with a potential customer. Theapplication may undergo underwriting to assess the eligibility of theparty and/or desired insured article or entity to be covered by theinsurance policy, and, in some cases, to determine any specific terms orconditions that are to be associated with the insurance policy, e.g.,amount of the premium, riders or exclusions, waivers, and the like. Uponapproval by underwriting, acceptance of the applicant to the terms orconditions, and payment of the initial premium, the insurance policy maybe in-force (i.e., the policyholder is enrolled).

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed:
 1. A computer-implemented method of processing vehicle collision risk information, the method comprising: receiving, at a hardware server, vehicle data including an indication of at least a current GPS (Global Positioning System) location of at least one of an automobile or a mobile device located within the automobile; accessing, by a processor located remotely from the at least one of the automobile or the mobile device, environment data associated with the current GPS location of the at least one of the automobile or the mobile device, the environment data including a historical record of automobile-animal collisions (i) at the current GPS location of the at least one of the automobile or the mobile device, or (ii) in a particular proximity to the current GPS location of the at least one of the automobile or the mobile device; based on the environment data and the current GPS location, executing a machine learning algorithm to determine, by the processor, that the automobile is at a risk greater than a threshold level for a non-avian animal collision, the threshold level being a level of risk greater than zero risk; generating, by the processor, a notification indicating the risk greater than the threshold level of collision with a non-avian animal; communicating, from the processor located remotely from the at least one of the automobile or the mobile device, the notification of the risk greater than the threshold level to the at least one of the automobile or the mobile device via a communications network so as to allow an operator of the automobile to be notified of the risk greater than the threshold level; receiving, at the hardware server, an animal collision report indicating that the automobile collided with a non-avian animal; and updating, by the processor, the machine learning algorithm according to the animal collision report.
 2. The computer-implemented method of claim 1, wherein communicating the notification to the at least one of the automobile or the mobile device comprises: communicating the notification to at least one of an onboard computer of the automobile or an electronic device associated with an operator of the automobile.
 3. The computer-implemented method of claim 1, wherein the vehicle data further includes: at least one of a speed of the automobile, automobile characteristics, or demographic information associated with an operator of the automobile.
 4. The computer-implemented method of claim 1, wherein the environment data further includes: at least one of ecological characteristics or roadway characteristics.
 5. The computer-implemented method of claim 1, wherein the environment data includes a first environment factor having a first specific weight and a second environment factor having a second specific weight, and wherein determining that the automobile is at the risk greater than the threshold level comprises: calculating an overall risk based on combining the first environment factor and the second environment factor.
 6. The computer-implemented method of claim 1, further comprising: identifying at least one of a time of day or a time of year, wherein determining that the automobile is at the risk greater than the threshold level is further based upon a portion of the environment data corresponding to the at least one of the time of day or the time of year.
 7. A system for processing vehicle collision risk information, comprising: a communication module adapted to communicate data; a memory adapted to store non-transitory computer executable instructions; a hardware server to store environment data; and a processor adapted to interface with the communication module, wherein the processor is configured to execute the non-transitory computer executable instructions to cause the system to: receive, via the communication module, vehicle data including an indication of at least a current GPS (Global Positioning System) location of at least one of an automobile or a mobile device located within the automobile, the processor being located remotely from the at least one of the automobile or the mobile device, access, via the processor, at least a portion of the environment data associated with the current GPS location of the at least one of the automobile or the mobile device, the environment data including a historical record of automobile-animal collisions (i) at the current GPS location of the at least one of the automobile or the mobile device, or (ii) in a particular proximity to the current GPS location of the at least one of the automobile or the mobile device, based on the environment data and the current GPS location, execute a machine learning algorithm to determine, via the processor, that the automobile is at a risk greater than a threshold level for a non-avian animal collision, the threshold level being a level of risk greater than zero risk, generate, via the processor, a notification indicating the risk greater than the threshold level of collision with a non-avian animal, communicate, via the communication module and a communications network, the notification of the risk greater than the threshold level to the at least one of the automobile or the mobile device located remotely from the processor so as to allow an operator of the automobile to be notified of the risk greater than the threshold level, receive, at the hardware server, an animal collision report indicating that the automobile collided with a non-avian animal, and update, via the processor, the machine learning algorithm according to the animal collision report.
 8. The system of claim 7, wherein to communicate the notification to the at least one of the automobile or the mobile device, the communication module is configured to: communicate the notification to at least one of an onboard computer of the automobile or an electronic device associated with an operator of the automobile.
 9. The system of claim 7, wherein to receive the vehicle data, the hardware server is configured to: receive at least one of a speed of the automobile, automobile characteristics, or demographic information associated with an operator of the automobile.
 10. The system of claim 7, wherein to access the environment data, the processor is configured to execute the non-transitory computer executable instructions to cause the processor to: access at least one of ecological characteristics or roadway characteristics.
 11. A computer-implemented method of issuing an alert associated with a vehicle-animal collision, the method comprising: accessing, via a processor, environment data that includes a historical record of automobile-animal collisions (i) at a current GPS location of at least one of an automobile or a mobile device located within the automobile, or (ii) in a particular proximity to the current GPS location of the at least one of the automobile or the mobile device, the processor being located remotely from the automobile; predicting, based upon the environment data and using a machine learning algorithm, (1) a geographical scope, (2) a temporal scope, and (3) a seasonal scope of an area at a risk greater than a threshold level of being associated with a collision with a non-avian animal when a non-avian animal is not detected at the automobile, the threshold level being a level of risk greater than zero risk; monitoring or identifying the current GPS location of the mobile device located within the automobile via the processor; monitoring or identifying a current time of day via the processor; monitoring or identifying a current time of year via the processor; and when, as determined by the processor, (a) the current GPS location of the mobile device matches or is within the geographical scope of the area at the risk greater than the threshold level, (b) the current time of day matches, is within, or coincides with the temporal scope of the area at the risk greater than the threshold level, and (c) the current time of year matches, is within, or coincides with the seasonal scope of the area at the risk greater than the threshold level, transmitting an alert indicating the risk greater than the threshold level from the processor located remotely from the automobile, via a communications network, to the mobile device located within the automobile; receiving an animal collision report indicating that the automobile collided with a non-avian animal; and updating, by the processor, the machine learning algorithm according to the animal collision report.
 12. The computer-implemented method of claim 11, wherein the alert comprises at least one of an audible alert, a visual alert, or a haptic alert.
 13. The computer-implemented method of claim 11, the method further comprising the processor determining whether or not the automobile is moving before transmitting the alert.
 14. The computer-implemented method of claim 11, wherein predicting the area at the risk greater than the threshold level of being associated with a collision with a non-avian animal is further based upon analyzing at least one of automobile accidents, driver characteristics, animal tendencies, weather data, calendar data, time of day, or geographical information.
 15. The computer-implemented method of claim 11, the method further comprising adjusting at least one of an insurance premium, a discount, or a reward based upon the mobile device being configured to receive the alert. 